Spectrally-resolved imaging is ubiquitous in numerous biological studies ranging from mapping synapse dynamics, to monitoring of intracellular signaling, and studying protein-protein interactions. The ability to independently monitor the lifetime and dynamics of cellular structures, such as the synapse, the nucleus, protein trafficking vesicles, and various other multicomponent complexes is critical to revealing their cellular function as well as their assembly and disassembly. While spectrally resolved visualization of 3-4 different proteins in the same cell is quite routine using confocal microscopy in fixed brain sections or in cell culture, dynamic multi-protein imaging in vivo remains a challenge, yet many intra- and inter- cellular interactions are dependent on the context of an intact tissue. Our goal is to develop and implement spectrally resolved technologies that are compatible with high throughput multiphoton microscopy to allow large volume, in vivo imaging of multicomponent subcellular structures. In the first two aims we propose testing two novel spectrometric approaches for large volume, high-speed imaging, with respective strengths and weaknesses, that can be tailored to tackle different imaging needs. In the third aim, we will develop a highly efficient wavelet based Poisson denoised spectral un-mixing algorithm that can potentially enhance both approaches by allowing accurate analysis of images with far lower SNR. Aim 1: Design a dispersive spectrometer (DS) to enable hyperspectral imaging in a multifocal multiphoton microscope (MMM) system utilizing multianode PMTs (MAPMT). Aim 2: Design a Fourier transform spectrometer (FTS) to enable hyperspectral imaging in MMM and wide-field multiphoton microscopy (WFMM) systems. Aim 3: Develop a morphology-guided, Poisson-denoised maximum likelihood (MLE) spectral decomposition algorithm to reduce SNR requirement of raw images.